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A method for predicting essential proteins using gene expression data

机译:利用基因表达数据预测必需蛋白质的方法

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Essential proteins are important for cell development and survival for an organism. Proteins are nothing but chain of amino acids. Amino acids are of three types-essential, non-essential and conditional. Essential proteins are nothing but a chain of essential amino acids which are to be predicted. Traditionally, many methods have been used to identify essential proteins, which are time consuming, need effortful experiment approach and noxious to the organisms targeted. In this work, two approaches - Mean Weighted Average (MWA) and Recursive Feature Elimination (RFE) is used, this approach will not ignore any values though the duplications. Accuracy using Recursive Feature Elimination must be more than Mean Weighted Average.
机译:必需蛋白质对于生物体的细胞发育和生存至关重要。蛋白质不过是氨基酸链。氨基酸分为必需,非必需和有条件的三种类型。必需蛋白质不过是一串必须预测的必需氨基酸。传统上,已经使用了许多方法来鉴定必需蛋白质,这些方法既费时,需要费力的实验方法又对目标生物有害。在这项工作中,使用了两种方法-平均加权平均值(MWA)和递归特征消除(RFE),尽管有重复,但该方法不会忽略任何值。使用递归特征消除的准确性必须大于平均加权平均值。

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